Drug rash with eosinophilia and systemic symptoms induced by lamotrigine therapy.

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Designing Logic Tensor Networks for Visual Sudoku puzzle classification

Given the increasing importance of the neurosymbolic (NeSy) approach in artificial intelligence, there is a growing interest in studying benchmarks specifically designed to emphasize the ability of AI systems to combine low-level representation learning with high-level symbolic reasoning. One such recent benchmark is Visual Sudoku Puzzle Classification, that combines visual perception with relational constraints. In this work, we investigate the application of Logic Tensork Networks (LTNs) to the Visual Sudoku Classification task and discuss various alternatives in terms of logical constraint formulation, integration with the perceptual module and training procedure

Choosing to migrate or migrating to choose: migration and labour choice in Albania

While sustainable economic growth, poverty reduction, and the management of migration flows are among the most pressing items on the policy agenda in Albania, very little systematic analysis exists of the income generating strategies of Albanian households within the emerging market economy, and how this relates to income dynamics, people's mobility and poverty. Results show that agricultural, migration and human capital assets have a differential impact across livelihood choices, and that this impact varies by gender and age. Two areas of policy concern derive from this analysis. First, migration is clearly crucial for the economic future of Albania, both in terms of financing economic development, serving as an informal safety net, and in reducing excess labour supply and poverty. The suggestion of a potential disincentive effect on labour effort and participation is however worrying, as it would have implications in terms of missed opportunities for development. Second, agriculture appears to be more of a survival strategy than part of a poverty exit strategy

Classification of multiple sclerosis patients based on structural disconnection: A robust feature selection approach

Background and Purpose Although structural disconnection represents the hallmark of multiple sclerosis (MS) pathophysiology, classification attempts based on structural connectivity have achieved low accuracy levels. Here, we set out to fill this gap, exploring the performance of supervised classifiers on features derived from microstructure informed tractography and selected applying a novel robust approach. Methods Using microstructure informed tractography with diffusion MRI data, we created quantitative connectomes of 55 MS patients and 24 healthy controls. We then used a robust approach-based on two classical methods of feature selection- to select relevant features from three network representations (whole connectivity matrices, node strength, and local efficiency). Classification accuracy of the selected features was tested with five different classifiers, while their meaningfulness was tested via correlation with clinical scales. As a comparison, the same classifiers were run on features selected with the standard procedure in network analysis (thresholding). Results Our procedure identified 11 features for the whole net, five for local efficiency, and seven for node strength. For all classifiers, the accuracy was in the range 64.5%-91.1%, with features extracted from the whole net reaching the maximum, and overcoming results obtained with the standard procedure in all cases. Correlations with clinical scales were identified across functional domains, from motor and cognitive abilities to fatigue and depression. Conclusion Applying a robust feature selection procedure to quantitative structural connectomes, we were able to classify MS patients with excellent accuracy, while providing information on the white matter connections and gray matter regions more affected by MS pathology